Sequential Auctions for the Allocation of Resources with Complementarities
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A Comparison among Bidding Algorithms for Multiple Auctions
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
ATTac-2001: A Learning, Autonomous Bidding Agent
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
The 2001 trading agent competition
Eighteenth national conference on Artificial intelligence
Bidding under uncertainty: theory and experiments
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
The effects of proxy bidding and minimum bid increments within eBay auctions
ACM Transactions on the Web (TWEB)
Setting discrete bid levels adaptively in repeated auctions
Proceedings of the 11th International Conference on Electronic Commerce
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Journal of Artificial Intelligence Research
Designing bidding strategies in sequential auctions for risk averse agents
Multiagent and Grid Systems - Advances in Agent-mediated Automated Negotiations
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There is much active research into the design of automated bidding agents, particularly for environments that involve multiple decoupled auctions. These settings are complex partly because an agent's strategy depends on information about other bidders'interests. When bidders' valuation distributions are not known ex ante, machine learning techniques can be used to approximate them from historical data. It is a characteristic feature of auctions, however, that information about some bidders'valuations is systematically concealed. This occurs in the sense that some bidders may fail to bid at all because the asking price exceeds their valuations, and also in the sense that a high bidder may not be compelled to reveal her valuation. Ignoring these "hidden bids" can introduce bias into the estimation of valuation distributions. To overcome this problem, we propose an EM-based algorithm. We validate the algorithm experimentally using agents that react to their environments both decision-theoretically and game-theoretically, using both synthetic and real-world (eBay) datasets. We show that our approach estimates bidders' valuation distributions and the distribution over the true number of bidders significantly more accurately than more straightforward density estimation techniques.